Include min and max values in the extremeintervals on quantile biner. #149
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Tests failed probably because of already existing errors... File tested is ok. |
vitorsrg
reviewed
Aug 16, 2020
| def p(new_df: pd.DataFrame) -> pd.DataFrame: | ||
| col_biner = lambda col: np.where(new_df[col].isnull(), nan, np.digitize(new_df[col], bins[col], right=right)) | ||
| bined_columns = {col: col_biner(col) for col in columns_to_bin} | ||
| bined_columns = {col: _fix_values(col_biner(col), len(bins[col])) for col in columns_to_bin} |
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i think np.clip(col_biner(col), 0, len(bins[col])) could be more idiomatic here
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the mock test seams to fail because this PR changes the function behaviour... maybe this change could be triggered by some parameter in a backwardly compatible way |
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@gabrieldi95 will you keep working on this? Seems that the tests are broken. Let me know, otherwise we will close this PR for now, later you can feel free to reopen it. |
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No updates in the last few months |
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Status
READY
Background context
quantile_biner would result in n+1 quantiles when the min or max values were equal to either the first or last quantile.
Description of the changes proposed in the pull request
Change values categorized as the '0' quantile to 1 and values categorizes as 'n+1' (n being the wanted number of quantiles)
to n.